Time-series data is often generated during operation of many types of systems. Time-series data is data that is associated with particular points in time or particular time intervals, often represented in the form of time stamps that are maintained with the data. In many situations, in order to allow analysis to occur, it is desirable to collect the time-series data generated by a system of interest and store the data in a data repository. The system of interest may be any system that can be monitored in some way to provide data for further analysis. For example, the weather, the economy, government and business systems (e.g., factory systems, computer systems, and so on) are all potential examples of systems of interest which may be monitored to provide data for further analysis.
Various solutions have been provided to meet the need for systems which can collect and analyze time-series data. However, present solutions have often proven unsatisfactory, particularly in situations where the data rate of the time-series data is very high or where the quantity of the time-series data is very large. Accordingly, there is a need for improved systems that are capable of efficiently collecting and analyzing time-series data. There is also a need for improved systems that are capable of receiving a serial description of a calculation to be performed, and then automatically decomposing the calculation into many constituent calculations which may be performed in parallel.
Additionally, when time-series data is collected and stored in a data repository, there is often a need to provide access to the time-series data so that a historical analysis of the time-series data can be performed. However, present solutions for providing access to time-series data have often proven slow or impractical, particularly in situations involving large volumes of time-series data. Accordingly, there is also a need for tools which allow large volumes of time-series data to be more easily and efficiently accessed to facilitate historical and/or real-time analysis of the data.
It should be appreciated that, although certain features and advantages are discussed, the teachings herein may also be applied to achieve systems and methods that do not necessarily achieve any of these features and advantages.